引用本文: | 陈金保,任刚,徐龙,胡文庆,郑阳,肖志怀.孤网模式下水电机组自适应最优PID控制器设计[J].控制理论与应用,2025,42(1):22~32.[点击复制] |
CHEN Jin-bao,REN Gang,XU Long,HU Wen-qing,ZHENG Yang,XIAO Zhi-huai.Design of adaptive optimal PID controller for hydropower units under frequency control mode[J].Control Theory and Technology,2025,42(1):22~32.[点击复制] |
|
孤网模式下水电机组自适应最优PID控制器设计 |
Design of adaptive optimal PID controller for hydropower units under frequency control mode |
摘要点击 2273 全文点击 46 投稿时间:2022-11-07 修订日期:2024-10-08 |
查看全文 查看/发表评论 下载PDF阅读器 |
DOI编号 10.7641/CTA.2023.20989 |
2025,42(1):22-32 |
中文关键词 水电机组 改进灰狼优化算法 自适应控制 Hopf分岔 神经网络 PID控制器 |
英文关键词 hydropower unit improved gray wolf optimizer adaptive control Hopf bifurcation neural network PID controller |
基金项目 国家自然科学基金项目(51979204), 中国博士后科学基金项目(2022T150498), 湖北省自然科学基金项目(2022CFD165)资助. |
|
中文摘要 |
为确保孤网模式下频率稳定性,水电站通常采用参数较小的固定PID(F-PID)控制,导致调节速度慢,难以实现全工况最优控制.针对这一问题,设计了一种基于改进灰狼优化算法(IGWO)和反向传播神经网络(BPNN)的水轮机调节系统(HTRS)自适应变PID控制器(V-PID),以在全工况下获得最优调节效果.首先,搭建高精度的HTRS仿真平台,并按水头和导叶开度变化范围划分工况.然后基于Hopf分岔理论确定各工况下PID参数约束条件及最大值.进一步,采用基于PID参数最大值数据集、综合ITAE指标和非线性收敛因子的IGWO计算出各工况下最优PID参数,并以最优PID参数作为BPNN样本数据,通过训练得到自适应V-PID控制器神经网络模型.最后,以某实际水电站为例,验证了V-PID控制器效果.仿真试验表明:基于V-PID控制器的非线性HTRS模型可根据工况变化在线自动调整PID参数,以结构简单、易实现为前提,实现了孤网模式下水电机组全工况最优控制. |
英文摘要 |
To ensure frequency stability under frequency control mode (FCM), hydropower stations usually adopt the fixed PID (F-PID) controller with small parameters, which leads to slow regulation speed and difficulty in achieving optimal control. To solve this problem, an adaptive variable PID controller (V-PID) for the hydraulic turbine regulation system (HTRS) is designed based on the improved grey wolf optimizer (IGWO) and back propagation neural network (BPNN) to obtain the optimal regulation effect under all operating conditions. First, a high-precision HTRS simulation platform is built and the working conditions are divided according to the variation range of the water head and guide vane opening. Further, the PID parameter constraint conditions and maximum values under each working condition are determined based on Hopf bifurcation theory. Next, IGWO, which is based on the maximum data set of PID parameters, comprehensive ITAE index, and nonlinear convergence factor, is used to calculate the optimal PID parameters, and the optimal PID parameters are used as sample data of BPNN to obtain the neural network model of adaptive V-PID controller. Finally, an actual hydropower station is taken as an example to verify the effectiveness of the V-PID controller. The simulation test shows that the nonlinear HTRS containing the V-PID controller can automatically adjust the PID parameters online according to the change of operating conditions, which realizes the optimal control under FCM on the premise of simple structure and easy realization. |
|
|
|
|
|